What Determines Household Use of Financial Transaction Services?

Ryan M. Goodstein

and

Sherrie L.W. Rhine

Federal Deposit Insurance Corporation*

October 17, 2013 FDIC’s 3rd Annual Consumer Research Symposium

*Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily reflect the views of the FDIC

Introduction Share of U.S. Families with a Bank Account, by Year

1.00

0.95

0.90

0.85

0.80

1989 1992 1995 1998 2001 2004 2007 2010

Source: Survey of Consumer Finances; triennial data between 1989-2010

Bank account ownership has been growing in recent decades

So has use of nonbank Alternative Financial Services (AFS) such as check cashing and money orders

(IBISWorld 2012; Apgar and Herbert 2004)

As of 2011, 25 percent of US households used a nonbank AFS within the past year, and 4 out of 5 AFS users also had a bank account (FDIC 2012)

2

Introduction

Our goal: To better understand the determinants of household financial transaction services use

This is an important issue for consumers and public policy Use of bank accounts is supported by consumer protection laws

and regulations which may not apply to AFS

Integration into the financial mainstream has benefits for households, and may also yield positive externalities at the community level

Why is AFS use growing? Lack of access to bank branches? (Caskey 1994)

Need for liquidity? (FDIC 2012; Gross et al. 2012)

More transparent fee structures? (FI$CA 2013)

3

Introduction

Previous literature shows that unbanked rates are higher among households with less income, less education, black, Hispanic, etc.

e.g. FDIC 2012; Rhine and Greene 2006; Barr 2009, 2011; Hogarth and O’Donnell 1997; Kooce-Lewis, Swagler, and Burton 1996; Caskey 1994, 1997

These household characteristics also are associated with higher rates of AFS use (e.g. FDIC 2012; Gross et al. 2012)

Not clear from previous research the extent to which household characteristics pick up the effects of other unobserved factors

4

Introduction

Key contributions of our study:

1. We address some of the omitted variable issues from previous literature. Specifically, we control for:

the presence of bank branches and AFS providers in the household’s local area

other neighborhood attributes which may influence household decision making

2. We examine bank account ownership and use of nonbank Alternative Financial Transaction Services (AFTS) products in a unified framework

5

Introduction

Our focus is on financial transaction services Bank account ownership

AFTS: money orders, check cashing, and remittances

Distribution of Households by Financial Transaction Services Use

Nbr Obs

Proportion Does Not Use AFTS

Uses AFTS All

Has Bank Account

Bank Only

28,289

0.746

Bank + AFTS

6,850

0.181

35,139

0.927

No Bank Account

Cash Only

988

0.026

AFTS Only

1,792

0.047

2,780

0.073

All 29,277

0.772

8,642

0.228

37,919

1.000

Notes: Unweighted proportions, based on analysis sample.

6

Introduction

Preview of Results

Household socio-economic characteristics (e.g. income, education) are the most important determinants of bank account ownership

Other demographic characteristics (e.g. race, ethnicity) have a much more important influence on whether the household uses nonbank AFTS in addition to a bank account, rather than on bank account ownership alone

The presence of bank branches and AFTS providers within 5 miles of the household’s residence has relatively modest effects on household use of financial transaction services

Other neighborhood attributes have, at most, a minor effect

7

Outline

Introduction

Data and Model

Results

Conclusions

8

Data

Household level data are from 2011 FDIC Unbanked/Underbanked Supplement to CPS Large, nationally representative sample Restricted-access geographic identifiers allow us to merge in localized data

“Local Market” for Financial Services Defined as the 5 mile radius around the centroid of the HH’s census tract Bank branch location data: FDIC’s 2011 SOD AFTS location data: Census’ 2011 ZCBP (as in Bhutta 2012) For both bank branch and AFTS, we include

Binary indicator: = 1 if number of locations > 0, = 0 otherwise Concentration: Number of locations per 1k population

Neighborhood attributes From 2011 ACS five-year estimates, tract population characteristics County-level property and violent crime rates (FBI’s 2011 UCR) County-level voter turnout rates for 2008 presidential election (CQ press)

9

Empirical Model

We estimate two alternative models (for household i in census tract j):

Bank Account Ownership (Logit)

= 𝑓(𝑧𝑗, 𝑦𝑖, 𝑥𝑖, 𝑎𝑗, 𝑠)

FTS Bundles (Multinomial Logit)

= 𝑓𝑘(𝑧𝑗, 𝑦𝑖, 𝑥𝑖, 𝑎𝑗, 𝑠)

where k = 1 if “Bank Only” (i.e. banked and does not use AFTS) k = 2 if “Bank plus AFTS” (i.e. banked and uses AFTS) k = 3 if “AFTS Only” (i.e. unbanked and uses AFTS) k = 4 if “Cash Only” (i.e. unbanked and does not use AFTS)

P 𝐵𝑎𝑛𝑘 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑗 = 1

P 𝐹𝑇𝑆𝑖𝑗 = 𝑘

10

Empirical Model

Control Variables:

zj: local market for financial services (indicators and per capita number of bank branches/AFTS)

yi : socio-economic characteristics of the household (income, employment, homeownership, education)

xi : other demographic characteristics of the household (age, race, ethnicity, nativity, HH structure)

aj : local neighborhood attributes (tract population characteristics; voter turnout; crime rates)

s : geographic fixed effects (state level)

11

Empirical Model

Note that we do not account for potential selection issues

For example, if banks and AFTS providers choose to locate in areas where demand for their services is high, the magnitude of these estimated effects on HH financial services choice will be biased upward in magnitude

This concern is mitigated somewhat by the fact we control for a wide range of local population characteristics in our empirical model

12

Outline

Introduction

Data and Model

Results

Conclusions

13

Results: P(Bank Account = 1) Estimates from “standard” model that controls only for HH

characteristics are similar to previous literature

Based on the fully specified model that includes controls for local financial services market and neighborhood attributes, we find:

Partial Effects of Bank Branch and AFTS on P(bank account = 1) effect of 1

Estimated SD inc. Category Variable Partial Effect from mean

Bank Branches At least 1 Bank Branch 0.011**

AFTS Providers

Bank Branches per capita

At least 1 AFTS

(0.005)

0.001

(0.002)

-0.003

[-0.0005]

AFTS per capita

(0.004)

-0.005

(0.008)

[-0.0005]

Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.35

Geographic access to bank branches and AFTS locations has a relatively small effect on household bank status

14

Results: P(Bank Account = 1)

Partial Effects of Neighborhood Attributes on P(bank account = 1) Effect of 1

SD inc. Estimated from

Category

Tract Population

Crime Rates

Voter Turnout

Variable

TrctPopShr LMI

TrctPopShr Age <= 34

TrctPopShr Black

TrctPopShr Hispanic

TrctPopShr FamHH SnglHd

TrctPopShr <= HS Diploma

County Property Crime

County Violent Crime

County Voter Turnout Rate

Partial Effect

-0.009

(0.011)

0.031**

(0.016)

-0.021**

(0.009)

0.002

(0.009)

-0.034*

(0.020)

-0.079***

(0.012)

-0.000

(0.000)

0.000

(0.001)

0.053***

(0.020)

mean

[-0.001]

[0.003]

[-0.004]

[0.000]

[-0.003]

[-0.014]

[-0.001]

[0.001]

[0.006]

Estimated effects of neighborhood attributes are generally quite small in terms of economic magnitude

One exception: local educational attainment

1 SD increase from the mean in the share of tract population with HS diploma or less (i.e. from 43 to 60 percent) reduces likelihood of being banked by 1.4 pp

Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.35

15

Results: P(Bank Account = 1) Partial Effects of Socio-economic Characteristics on P(bank account = 1)

Estimated Category Variable Partial Effect

HH Income Middle Income -0.013***

(0.004)

Moderate Income -0.028***

(0.004)

Low Income -0.075***

(0.004)

HH LF Status Part-Time -0.011***

(0.004)

Unemployed -0.053***

(0.005)

NILF -0.040***

(0.003)

HH Tenure Non-Homeowner -0.052***

(0.003)

HH Education No HS Diploma -0.090***

(0.004)

HS Diploma -0.050***

(0.003)

Some College -0.025***

(0.003) Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.35

Effects of socio-economic characteristics are large in magnitude

Low income HH 8 pp less likely to be banked (relative to high income)

HH with no HS diploma are 9 pp less likely to be banked (relative to college degree)

16

Results: P(Bank Account = 1) Partial Effects of Selected Other Demographic

Characteristics on P(bank account = 1)

Estimated Category Variable Partial Effect

HH Age Age 34 or less -0.006

(0.004)

Age 55 to 64 0.031***

(0.004)

Age 65 or older 0.062***

(0.003)

HH Race Black -0.037***

(0.005)

Asian 0.014**

(0.007)

Other -0.026***

(0.007)

HH Ethnicity Hispanic -0.035***

(0.005)

HH Nativity Non-Citizen -0.022***

(0.005) Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.35

Demographic effects are important but generally smaller in magnitude than socio-economic factors

17

Results: FTS Bundles

We now shift to the MNL model; dependent variable is the bundle of financial transaction services used (Bank Only; Bank plus AFTS; AFTS Only; Cash Only)

18

Results: FTS Bundles Partial Effects of Bank Branch and AFTS on use of FTS Bundles

Outcome

Bank AFTS Cash Category Variable Bank Only plus AFTS Only Only

Bank Branches At least 1 Bank Branch 0.017** -0.007 -0.007* -0.004

(0.008) (0.008) (0.004) (0.003)

Bank Branches per capita 0.004 -0.002 -0.000 -0.002

(0.003) (0.003) (0.002) (0.003)

[0.003] [-0.002] [-0.000] [-0.002]

AFTS Providers At least 1 AFTS -0.000 -0.003 0.002 0.000

(0.007) (0.006) (0.004) (0.003)

AFTS per capita -0.056** 0.047** 0.007 0.002

(0.025) (0.021) (0.007) (0.006)

[-0.005] [0.005] [0.001] [0.000]

Proportion of Sample in Category 0.75 0.18 0.05 0.03 Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.19. Number in brackets is estimated effect of a 1 SD increase from the mean.

As in bank account model, effects of Bank branch and AFTS are fairly small in magnitude

Also as in bank account model, effects of other neighborhood attributes are generally quite small (not shown)

19

Results: FTS Bundles Partial Effects of Selected Socio-Economic Characteristics on FTS Bundles

Outcome

Bank AFTS Cash Category Variable Bank Only plus AFTS Only Only

HH Income Middle Income -0.017** 0.004 0.010*** 0.002

(0.007) (0.007) (0.003) (0.003)

Moderate Income -0.045*** 0.018*** 0.019*** 0.008***

(0.007) (0.007) (0.003) (0.003)

Low Income -0.084*** 0.010 0.050*** 0.024***

(0.007) (0.007) (0.003) (0.003)

HH Tenure Non-Homeowner -0.116*** 0.065*** 0.036*** 0.016***

(0.005) (0.005) (0.002) (0.002)

HH Education No HS Diploma -0.124*** 0.035*** 0.062*** 0.027***

(0.008) (0.008) (0.004) (0.003)

HS Diploma -0.075*** 0.025*** 0.038*** 0.012***

(0.006) (0.006) (0.003) (0.002)

Some College -0.051*** 0.026*** 0.023*** 0.002

(0.006) (0.005) (0.002) (0.002)

Proportion of Sample in Category 0.75 0.18 0.05 0.03 Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.19

Estimated effects of socio-economic characteristics are large in magnitude

Households at lower levels of socio-economic status are relatively more likely to be AFTS Only or Cash Only (i.e. no bank account) and less likely to be in the Bank+AFTS group

20

Results: FTS Bundles Partial Effects of Selected Demographic Characteristics on use of FTS Bundles

Outcome

Bank AFTS Cash Category Variable Bank Only plus AFTS Only Only

HH Age Age 34 or less -0.004 -0.001 0.005 0.001

(0.006) (0.006) (0.003) (0.002)

Age 55 to 64 0.039*** -0.009 -0.021*** -0.010***

(0.006) (0.006) (0.003) (0.003)

Age 65 or older 0.126*** -0.065*** -0.047*** -0.015***

(0.006) (0.006) (0.002) (0.002)

HH Race Black -0.154*** 0.116*** 0.020*** 0.018***

(0.010) (0.009) (0.004) (0.003)

Asian 0.017 -0.001 -0.030*** 0.014**

(0.012) (0.011) (0.004) (0.006)

Other -0.089*** 0.064*** 0.017*** 0.009*

(0.014) (0.014) (0.006) (0.005)

HH Ethnicity Hispanic -0.078*** 0.044*** 0.017*** 0.018***

(0.009) (0.009) (0.004) (0.004)

HH Nativity Non-Citizen -0.074*** 0.050*** 0.012*** 0.013***

(0.010) (0.010) (0.004) (0.004)

Proportion of Sample in Category 0.75 0.18 0.05 0.03 Notes: Partial effects based on estimates from underlying logit model. Specification includes full set of controls. N = 37919; Pseudo R2 = 0.19

Estimated effects on demographic characteristics are large, and at least as important as socio-economic attributes (unlike the Bank Account model)

Racial and ethnic minority groups are relatively more likely to be Bank+AFTS and less likely to be AFTS Only or Cash Only 21

Results: Discussion

In summary, our results indicate that:

Socio-economic characteristics (e.g. income, education) are the most important determinants of bank account ownership

Other demographic characteristics (e.g. race, ethnicity) have a much more important influence on whether or not a household uses nonbank AFTS in addition to a bank account.

We explore this further by stratifying sample on income

Is there heterogeneity in the magnitude of effects?

22

Results: Stratify by HH Income Partial Effects of Selected HH Characteristics on Bank Status

Model and Dependent Variable

Sample

Logit: P(Bank Account)

Low Income High Income

HH Education No HS Diploma

HS Diploma

Some College

HH Age Age 34 or less

Age 55 to 64

Age 65 or older

HH Race Black

Asian

Other

HH Ethnicity Hispanic

-0.198***

(0.014)

-0.136***

(0.014)

-0.079***

(0.014)

-0.011*

(0.007)

0.062***

(0.009)

0.157***

(0.009)

-0.065***

(0.009)

0.026

(0.021)

-0.056***

(0.014)

-0.063***

(0.009)

-0.022***

(0.006)

-0.015***

(0.005)

-0.011**

(0.005)

0.004

(0.004)

0.002

(0.003)

0.005

(0.005)

-0.009**

(0.004)

-0.009*

(0.005)

Sample Size

Proportion of Sample in Category

14493

0.84

9009

0.996

Among low-income households, household characteristics are associated with large differences in bank ownership rates

Among high-income households, effects of race, ethnicity are quite small in magnitude

Implications:

For more marginal HH, demographic characteristics have some influence

But above some minimum threshold level of income and education, nearly all households have a bank account regardless of their demographic characteristics

23

Results: Stratify by HH Income Partial Effects of Selected HH Characteristics on FTS Bundles

Model and Dependent Variable

Sample

MNL: P(Bank

Low Income

Only)

High Income

MNL: P(Ba

Low Income

nk + AFTS)

High Income

HH Education No HS Diploma

HS Diploma

Some College

HH Age Age 34 or less

Age 55 to 64

Age 65 or older

HH Race Black

Asian

Other

HH Ethnicity Hispanic

-0.193***

(0.015)

-0.140***

(0.014)

-0.093***

(0.014)

-0.016*

(0.010)

0.061***

(0.011)

0.225***

(0.011)

-0.164***

(0.012)

0.093***

(0.025)

-0.101***

(0.020)

-0.074***

(0.014)

-0.111***

(0.019)

-0.049***

(0.010)

-0.035***

(0.009)

0.010

(0.011)

0.011

(0.009)

0.051***

(0.014)

-0.114***

(0.014)

-0.002

(4.284)

0.012

(7.156)

-0.062***

(0.015)

-0.010

(0.014)

-0.002

(0.012)

0.009

(0.013)

0.005

(0.009)

0.000

(0.010)

-0.063***

(0.011)

0.096***

(0.011)

-0.042*

(0.024)

0.046**

(0.018)

0.013

(0.013)

0.099***

(0.019)

0.041***

(0.010)

0.029***

(0.009)

-0.009

(0.011)

-0.010

(0.009)

-0.049***

(0.014)

0.108***

(0.014)

0.058

(1.950)

0.043

(3.120)

0.057***

(0.015)

Sample Size

Proportion of Sample in Category

14493

0.63

9009

0.86

14493

0.21

9009

0.13

Even among high-income households, certain HH characteristics have an important influence on HH choice of FTS bundles

Note: Estimates for AFTS Only and Cash Only are omitted from this table.

24

Results: Alternative Specifications

Prepaid/payroll cards We have ignored such cards in our analysis (use is fairly low in

our sample) But these cards are gaining in popularity and may be a viable

option for households considering how to manage their finances We tried two alternative specifications, treating use of

prepaid/payroll cards as equivalent to using: 1. bank account 2. AFTS

In both cases, findings are qualitatively similar

Other robustness checks included: Alt definitions of local area (e.g. 2 mi; 10 mi; 25 mi) Alt measures of financial service providers (e.g. nbr of

banks/AFTS locations) Alt treatment of money orders (or remittances), where use does

not disqualify household from being categorized as “Bank Only”

25

Results: Other Caveats

Our estimates indicate that geographic access to bank branches/AFTS has a relatively minor influence on HH choice of financial transaction services

May be due in part to measurement error, but several alternative measures yielded same qualitative finding

Other unobserved supply side factors may still be important (e.g. fee structures of bank accounts/AFTS; access to funds)

Our MNL model imposes assumption of IIA

This may be an issue, at least in theory. In practice – not clear.

In future work we will evaluate the extent to which this is an issue, and potentially use a specification that relaxes the IIA assumption

26

Outline

Introduction

Data and Model

Results

Conclusions

27

Conclusions

The presence of bank branches and AFTS providers within 5 miles of the household’s residence has relatively modest effects on household use of financial transaction services

Other neighborhood attributes have, at most, a minor effect

28

Conclusions

Household socio-economic characteristics (e.g. income, education) are the most important determinants of bank account ownership

Other demographic characteristics (e.g. race, ethnicity) have a more important influence on whether the household uses nonbank AFTS in addition to a bank account, rather than on bank account ownership alone

Given that we control for the local market for financial services, and for other neighborhood attributes, the specific mechanisms driving the finding that black and Hispanic households are more likely to use AFTS in combination with bank accounts are not obvious

29

- the end -

Thank you!

30

Appendix: Selected Descriptive Stats

For all characteristics that vary at the person-level, we use householder characteristics to represent the household

We drop roughly 15% of households that participated in the CPS supplement due to missing data (primarily due to missing geography; sample means change minimally) 37919 obs

Selected Descriptive Statistics

Category Variable ALL

By Bank Status

Banked Unbanked

By Financial Services Type

Bank Bank AFTS Cash Only plus AFTS Only Only

Bank Branches At least 1 Bank Branch 0.89 0.89 0.90 0.89 0.88 0.90 0.89

Bank Branches per capita (1k) 0.33 0.33 0.29 0.34 0.31 0.30 0.28

AFTS Providers At least 1 AFTS 0.66 0.65 0.74 0.64 0.68 0.75 0.72

AFTS per capita (1k) 0.05 0.05 0.07 0.05 0.06 0.08 0.07

Tract Population TrctPopShr LMI 0.42 0.41 0.54 0.40 0.46 0.55 0.54

TrctPopShr Age <= 34 0.46 0.46 0.50 0.45 0.48 0.50 0.50

TrctPopShr Black 0.11 0.11 0.24 0.09 0.16 0.24 0.23

TrctPopShr Hispanic 0.12 0.12 0.22 0.11 0.15 0.22 0.22

TrctPopShr FamHH Single Head 0.17 0.16 0.24 0.16 0.19 0.25 0.24

TrctPopShr HS Diploma or Less 0.43 0.42 0.55 0.41 0.46 0.55 0.54

Crime Rates County Property Crime 28.62 28.39 31.55 28.11 29.53 31.80 31.10

County Violent Crime 3.89 3.82 4.72 3.74 4.16 4.79 4.58

Voter Turnout County Voter Turnout Rate 0.60 0.61 0.57 0.61 0.59 0.57 0.57

Number of Observations 37919 35139 2780 28289 6850 1792 988

Notes: unweighted proportions, analysis sample. Crime rates are per 100k population. 31